Today’s AI landscape moves so fast and providers differ so much that vendor lock-in can become expensive. You need a clean, testeable way to add AI without tying your architecture to one SDK.
The solution to this problem is a model-agnostic solution.
Nuggets to use (you need to click to see preliminar versions):
- Microsoft.Extensions.AI - This nugget implements
IChatClientinterface, which is an abstraction to use several LLM providers, from ChatGPT to Ollama. - Microsoft.Extensions.AI.OpenAI
- OllamaSharp (previously Microsoft.Extensions.AI.Ollama)
You’ll need to go to https://platform.openai.com/ to set up a project, billing, and get an openAI API key.
This repository is a test implementation which connects to OpenAi’s ChatGPT and is able to send prompts.
Best Practices
- Keep inputs short and specific
- Validate outputs with regex/JSON schema. Reject or re-ask when invalid
- Log prompts, token counts, latency and provider responses
- Improve cost ops. Cache results, batch requests and prefer smaller models by default
- Don’t commit or send secrets or personal information
- Failover. Implement timeouts, retries, and fallback models
- LLMs are stateless; maintaining and reconstructing conversational context is a developer’s responsibility (chat history or memory abstractions)
Security
- prompt injection: beware with malicious prompts to subvert model guardrails, steal data or execute unintended actions
- LLMs may leak private or internal data via crafted prompts
- Training data poisoning may be injected by malicious actors
- DoS and rate limiting: prevent overuse / abuse
Reference(s)
https://roxeem.com/2025/09/04/the-practical-net-guide-to-ai-llm-introduction/
https://roxeem.com/2025/09/08/how-to-correctly-build-ai-features-in-dotnet/